Investigating the Contribution of Molecular-Enriched Functional Connectivity to Brain-Age Analysis

Brain-age prediction from neuroimaging data provides a proxy of biological aging, yet most models rely on structural magnetic resonance imaging (MRI), a modality that captures macroanatomy but offers limited biological specificity. We tested whether integrating molecular-enriched functional connectivity (FC) from resting-state functional MRI (rs-fMRI) data improves brain-age prediction and biological explainability. We analyzed MRI data of 2120 healthy adults (1243/877 F/M; 18-90 years) from three public datasets. Molecular-enriched connectivity maps were derived with Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) using receptor-density templates for the dopamine (DAT), norepinephrine (NET), and serotonin (SERT) transporter systems. Support vector regression models were applied to predict chronological age from molecular-enriched FC, structural morphometry, or both combined. The effect of multi-site variability was mitigated via ComBat harmonization with and without Empirical Bayes pooling. We additionally conducted a common-parcellation analysis to assess the impact of differing parcellations between modalities. Single-transporter molecular-enriched FC explained up to 51% of age variance. The most predictive transporter varied by dataset, with DAT dominating in the harmonized and common-parcellation settings. Combining the three molecular-enriched maps consistently improved prediction over any single map and increased explained variance up to 64%. Structural morphometry remained the strongest single modality overall. In the merged multi-site cohort using a common parcellation, adding transporter-enriched FC to structural features yielded a small but consistent reduction in prediction error (mean absolute error (MAE) from 6.02 to 5.81 years), supporting limited complementarity between the two modalities. Residual-level paired comparisons across repeated cross-validation confirmed that this improvement is statistically reliable but modest in magnitude. In contrast, when different parcellations were applied, incorporating molecular-enriched FC into brain age prediction resulted in a 2% higher MAE compared to structural morphometry alone, suggesting that parcellation mismatch may obscure the functional contributions. In conclusion, molecular-enriched FC is a feasible and biologically informative extension to brain-age modeling; however, its added predictive value over structural morphometry was modest and depended on harmonization and atlas alignment.